LLM-Assisted Binaryen Optimization Workflow #415

Hi!

I have a question about the optimization workflow used for go-sqlite3.

Do you use any kind of automated profiling + aggressive optimization of hot paths with an LLM on top of Binaryen?

At first glance, this approach may seem prohibitively expensive, but I think the economics are actually quite different. The expensive part only needs to be done once per major upstream release. When a new minor upstream version comes out, you can simply show the model the diff from the previous optimization and say: "Optimize the new version in the same way!" Rewriting code based on an existing reference is something that doesn't require a top-tier LLM.

I experimented with a similar approach in my wasm2go zlib port. I needed compression to produce a bit-for-bit compatible stream with the original C zlib, so replacing the algorithm with a different implementation wasn't an option.

I aggressively optimized only the compression path, since I didn't need decompression. I asked LLM to optimize like top demo scene hacker. I didn't even need the latest models like Opus; the Gemini 3 series handled it perfectly. The resulting zlib4go compression performance ended up even slightly faster than the original C zlib in my benchmark. The project is here:

https://github.com/unxed/zlib4go

The repository also contains the benchmark results and the generated Go/assembly implementation.

I'm curious whether you've considered this kind of workflow for go-sqlite3, or perhaps are already using something similar.

PS: I'm using your excellent lib in
https://github.com/unxed/tar
https://github.com/unxed/zipper
https://github.com/unxed/f4
Great job, thanks!